A comprehensive index of artificial intelligence and machine-learning research with AI-generated summaries, citation metrics, and direct links to papers and code.
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Q-dit introduces a post-training quantization method for diffusion transformers that reduces memory and latency while maintaining generation quality.
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PostTrainBench benchmarks how well LLM agents can autonomously perform LLM post-training under bounded compute constraints (10 hours on one H100 GPU).
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RECAP post-training teaches large reasoning models to override flawed reasoning trajectories, improving alignment.
Komal Kumar, Tajamul Ashraf, Omkar Thawakar, et al.
A systematic survey of post-training techniques for LLMs, covering fine-tuning, reinforcement learning, and test-time scaling, with a public repository for tracking developments.
Guanghao Ye, Khiem Pham, Xinzhi Zhang, et al.
Proposes RLSP, a post-training framework that decouples exploration and correctness signals during PPO to enable emergent reasoning behaviors in LLMs.
Keming Wu, Baoyi Wang, Kaichen Zhang, et al.
StreamOPD is a post-training recipe combining verifiable streaming-video data, thinking-mode on-policy distillation, and instruct-mode deployment, improving streaming video understanding without inference-time memory or retrieval.
Xiaoyu Zhu, Xinke Deng, Suresh Taddewadikar, et al.
Introduces Internalized Visual Thinking (IVT), a post-training framework that predicts latent future frames during training to enable efficient, direct-answer video reasoning at inference, outperforming Visual CoT with 5x lower latency.
He-Yen Hsieh, H. T. Kung
ReRound is a post-training quantization method that uses a conditional diffusion model to resolve midpoint ambiguity in round-to-nearest, improving low-bit LLM quantization without calibration data.
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Introduces RIPT-VLA, a reinforcement-learning-based interactive post-training paradigm for fine-tuning pretrained Vision-Language-Action models.
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This paper investigates reinforcement learning for interactive tool-using agents, highlighting user model fine-tuning as a critical component for training such agents.
Guiyao Tie, Zeli Zhao, D. Song, et al.
First comprehensive survey of post-training LLMs, covering fine-tuning, alignment, reasoning, efficiency, and integration, with a taxonomy and agenda for Large Reasoning Models.
Zeyang Sha, Shiwen Cui, Weiqiang Wang
SEM is a post-training RL framework that explicitly trains LLMs to optimize search usage, reducing redundant search while maintaining or improving answer accuracy.